Instructions for fused multiply-add operations with variable precision input operands

ABSTRACT

Disclosed embodiments relate to instructions for fused multiply-add (FMA) operations with variable-precision inputs. In one example, a processor to execute an asymmetric FMA instruction includes fetch circuitry to fetch an FMA instruction having fields to specify an opcode, a destination, and first and second source vectors having first and second widths, respectively, decode circuitry to decode the fetched FMA instruction, and a single instruction multiple data (SIMD) execution circuit to process as many elements of the second source vector as fit into an SIMD lane width by multiplying each element by a corresponding element of the first source vector, and accumulating a resulting product with previous contents of the destination, wherein the SIMD lane width is one of 16 bits, 32 bits, and 64 bits, the first width is one of 4 bits and 8 bits, and the second width is one of 1 bit, 2 bits, and 4 bits.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/735,381 filed Jan. 6, 2020, which is a continuation of U.S. patentapplication Ser. No. 15/940,774 filed Mar. 29, 2018, now U.S. Pat. No.10,528,346, which are hereby incorporated by reference.

FIELD OF THE INVENTION

The present disclosure pertains to the field of processing logic,microprocessors, and associated instruction set architectures, and morespecifically, to instructions for fused multiply-add operations withvariable-precision input operands.

BACKGROUND

Deep Learning is a class of machine learning algorithms. Deep learningarchitectures, such as deep neural networks, have been applied to fieldsincluding computer vision, speech recognition, natural languageprocessing, audio recognition, social network filtering, machinetranslation, bioinformatics and drug design.

Inference and training, two tools used for deep learning, are tendingtowards low-precision arithmetic. Maximizing throughput of deep learningalgorithms and computations may assist in meeting the needs of deeplearning processors, for example, those performing deep learning in adata center.

Quad virtual neural network instructions (QVNNI) are a type of fusedmultiply-add (FMA) operation that are useful in a deep learning context.Low-precision QVNNI operations, such as those using 8-bit activationswith weights being as low as 2-bits or 4-bits, are expected to lead tosufficient training performance. But traditional CPU and GPU instructionset architectures keep to a 32-bit lane for all operations and requiresymmetric operands: both inputs having the same precision, which limitsthe ability to gain a performance advantage by going to 2-bit and 4-bitweights.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1 is a block diagram illustrating processing components forexecuting a fused multiply-add (FMA) instruction, such as a quad virtualneural network instruction (QVNNI), according to some embodiments;

FIG. 2 is a block diagram illustrating execution circuitry to process anFMA instruction, according to some embodiments;

FIG. 3 is a block diagram illustrating execution circuitry to process anFMA instruction, according to some embodiments;

FIG. 4A is a block diagram illustrating execution circuitry to process aVNNI_8_4 FMA instruction, according to some embodiments;

FIG. 4B is a block diagram illustrating execution circuitry to process aVNNI_8_2 FMA instruction, according to some embodiments;

FIG. 4C is a block diagram illustrating execution circuitry to process aVNNI_8_1 FMA instruction, according to some embodiments;

FIG. 4D is a block diagram illustrating execution circuitry to process aVNNI_4_2 FMA instruction, according to some embodiments;

FIG. 4E is a block diagram illustrating execution circuitry to process aVNNI_4_1 instruction, according to some embodiments;

FIG. 4F is a block diagram illustrating execution circuitry to process aK-way VNNI_8_2 FMA instruction, according to some embodiments;

FIG. 5 is pseudocode illustrating execution circuitry to processVNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMA instructions,according to some embodiments;

FIG. 6 is a process flow diagram illustrating execution of an FMAinstruction, according to some embodiments;

FIG. 7 is a format of an FMA instruction, according to some embodiments;

FIGS. 8A-8B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according toembodiments of the invention;

FIG. 8A is a block diagram illustrating a generic vector friendlyinstruction format and class A instruction templates thereof accordingto embodiments of the invention;

FIG. 8B is a block diagram illustrating the generic vector friendlyinstruction format and class B instruction templates thereof accordingto embodiments of the invention;

FIG. 9A is a block diagram illustrating an exemplary specific vectorfriendly instruction format according to embodiments of the invention;

FIG. 9B is a block diagram illustrating the fields of the specificvector friendly instruction format that make up the full opcode fieldaccording to one embodiment of the invention;

FIG. 9C is a block diagram illustrating the fields of the specificvector friendly instruction format that make up the register index fieldaccording to one embodiment of the invention;

FIG. 9D is a block diagram illustrating the fields of the specificvector friendly instruction format that makes up an augmentationoperation field according to one embodiment of the invention;

FIG. 10 is a block diagram of a register architecture according to oneembodiment of the invention;

FIG. 11A is a block diagram illustrating both an exemplary in-orderpipeline and an exemplary register renaming, out-of-orderissue/execution pipeline according to embodiments of the invention;

FIG. 11B is a block diagram illustrating both an exemplary embodiment ofan in-order architecture core and an exemplary register renaming,out-of-order issue/execution architecture core to be included in aprocessor according to embodiments of the invention;

FIGS. 12A-B illustrate a block diagram of a more specific exemplaryin-order core architecture, which core would be one of several logicblocks (including other cores of the same type and/or different types)in a chip;

FIG. 12A is a block diagram of a single processor core, along with itsconnection to the on-die interconnect network and with its local subsetof the Level 2 (L2) cache, according to embodiments of the invention;

FIG. 12B is an expanded view of part of the processor core in FIG. 12Aaccording to embodiments of the invention;

FIG. 13 is a block diagram of a processor that may have more than onecore, may have an integrated memory controller, and may have integratedgraphics according to embodiments of the invention;

FIGS. 14-17 are block diagrams of exemplary computer architectures;

FIG. 14 shown a block diagram of a system in accordance with oneembodiment of the present invention;

FIG. 15 is a block diagram of a first more specific exemplary system inaccordance with an embodiment of the present invention;

FIG. 16 is a block diagram of a second more specific exemplary system inaccordance with an embodiment of the present invention;

FIG. 17 is a block diagram of a System-on-a-Chip (SoC) in accordancewith an embodiment of the present invention; and

FIG. 18 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to embodimentsof the invention.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth.However, it is understood that embodiments of the invention may bepracticed without these specific details. In other instances, well-knowncircuits, structures and techniques have not been shown in detail inorder not to obscure the understanding of this description.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Disclosed embodiments maximize execution throughput of a virtual neuralnetwork (VNNI) fused multiply-add (FMA) instruction having variableprecision inputs, such as 8 bits, 4 bits, 2 bits, and 1 bit. Someembodiments use single-instruction multiple data (SIMD) processing lanesthat have 32-bit lane widths, and that span greater than 32 bits for afirst source operand, such as a VNNI inputs vector, while sticking to a32-bit lane for an output and for a second source operand, such as aVNNI weights vector. The disclosed FMA instructions are expected toyield a performance gain over FMA instructions having symmetric, 8-bitinput and weight operands. Disclosed FMA instructions supporting inputswith different precision are sometimes referred to herein as asymmetricFMA instructions.

In some embodiments, FMA instructions are executed by executioncircuitry having SIMD processing lanes using a grid of FMA circuits. Bynot requiring the FMA circuits to use the same precision for the inputoperands, disclosed embodiments avoid limiting a performance gain bylimiting operations to use the precision of the highest-precisionoperands. In particular, disclosed embodiments speed up a low-precisionFMA by a factor proportional to the lowest-precision operand, such asthe weight. For example, using a 4-bit weight instead of an 8-bit weightis expected to yield a roughly 2× improvement, while using a 2-bitweight or a 1-bit weight is expected to yield a roughly 4× or 8×improvement, respectively.

Some disclosed embodiments describe a flexible choice of FMAinstructions, including QVNNI-8-2, QVNNI-8-2, QVNNI-8-1, QVNNI-4-2, andQVNNI-4-1, which provide a balance between size of operands which can bemanaged in an execution circuitry, and accuracy. Some disclosedembodiments provide a single, FMA instruction having fields to specifythe size of the inputs and the size of the weights. Disclosedembodiments advantageously avoid limiting execution to a same-sized bitlane for all operations and avoid requiring symmetric operands (bothinputs having the same precision). By removing such restrictions,disclosed embodiments enable increased throughput.

Disclosed embodiments define an asymmetric FMA instruction, such asQVNNI-8-2, to provide 4 outputs/instr*16 weights/output*16 SIMDlanes=1024 FMA operations per cycle throughput, which is 4× that of asymmetric FMA instruction, such as QVNNI with 8-bit operand limitation(4 outputs/instr*4 weights/output*16 SIMD lanes=256 FMA per cyclethroughput).

FIG. 1 is a block diagram illustrating processing components forexecuting a fused multiply-add (FMA) instruction, such as a quad virtualneural network instruction (QVNNI) according to some embodiments. Asillustrated, storage 102 stores a QVNNI instruction 103 to be executed.

The instruction is received by decode circuit 105. For example, decodecircuit 105 receives this instruction from fetch circuit 104. Theinstruction, as described further below, has fields specifying anopcode, an input vector, a weights vector, a destination, and a weightsize comprising one of one, two, and four bits. Decode circuit 105decodes the instruction into one or more operations. In someembodiments, this decoding includes generating a plurality ofmicro-operations to be performed by execution circuit (such as executioncircuit 109). The decode circuit 105 also decodes instruction prefixes(if used). Execution circuit 109 is further described and illustratedwith respect to FIG. 2, FIG. 3, FIGS. 4A-F, FIG. 11A, FIG. 11B, FIG.12A, FIG. 12B, and FIG. 13, below.

In some embodiments, register renaming, register allocation, and/orscheduling circuit 107 provides functionality for one or more of: 1)renaming logical operand values to physical operand values (e.g., aregister alias table in some embodiments), 2) allocating status bits andflags to the decoded instruction, and 3) scheduling the decodedinstruction for execution on execution circuit out of an instructionpool (e.g., using a reservation station in some embodiments).

Registers (register file) and/or memory 108 store data as operands ofthe instruction to be operated on by the execution circuit. Exemplaryregister types include packed data registers, general purpose registers,and floating point registers.

In some embodiments, write back circuit 111 commits the result of theexecution of the decoded QVNNI instruction.

FIG. 2 is a block diagram illustrating execution circuitry to process afused multiply-add (FMA) instruction, according to some embodiments. Asshown, FMA instruction 202 is a VNNI_8_4 FMA instruction having fieldsto specify an opcode, a destination, and first and second source vectorshaving first and second widths, respectively. In the context of virtualneural networks, and as shown, the first source vector can represent aninput vector, and the second source vector can represent a weightsvector. The opcode also includes suffixes to specify an input size, 8bits, and a weight size, 4 bits. Here, the identified first source isinputs [63:0], consisting of eight, 8-bit input elements. The identifiedsecond source is weights [31:0], consisting of eight 4-bit weightelements. In some embodiments, one or more of the identified firstsource, second source, and destination are stored in registers, such asin a register file of a processor, for example as illustrated anddiscussed below with reference to FIG. 10. In some embodiments, one ormore of the identified first source, second source, and destination arestored in a memory location.

In operation, execution circuit 208 executes the decoded instruction bygenerating a product of each input-sized element of the identified firstsource vector (inputs 204) and a corresponding weight-sized element ofthe identified second source vector (weights 206), and accumulating thegenerated products with previous contents of the identified destination216. As used herein, the term “corresponding” is used to describe vectorelements that occupy a same relative position with their respectivevectors. Here, the input size is 8 bits, as specified by the ‘8’ in theopcode of FMA instruction 202 and the weight size is 4 bits, asspecified by the ‘4’ in the opcode of FMA instruction 202. In otherwords, execution circuit 208 generates a destination output as describedby Equation 1, below:

dest+=dest+in0*wt0+in1*wt1+in2*wt2+in3*wt3+in4*wt4+in5*wt5+in6*wt6+in7*wt7  Equation1

In some embodiments, execution circuit 208 includes rounding circuitryto round the result generated by FMA7 to fit within the number of bitsof destination 216, which here is 32 bits. In the case of floating pointarithmetic, execution circuitry may round the resulting sum according tothe IEEE 754 floating point standard, established in 1985 and updated in2008 by the Institute of Electrical and Electronics Engineers. The IEEE754 floating point standard defines rounding rules to be applied,including round to nearest with ties to even, round to nearest with tiesaway from zero, toward 0, toward positive infinity, and toward negativeinfinity. In some embodiments, execution circuit 208 includes asoftware-accessible rounding control register (not shown) to specify therounding rule to apply.

In some embodiments, execution circuit 208 checks for saturation andsaturates the resulting sum to a predefined maximum.

In some embodiments, as here, execution circuit 208 utilizes one or moresingle-instruction, multiple data (SIMD) processing lanes that perform asame operation on multiple data points at the same time. In someembodiments, execution circuit 208 includes multiple SIMD processinglanes, for example, 32 lanes, to perform a same operation on 32 lanes ofdata. In some embodiments, as here, an SIMD processing lane has a lanewidth of 32 bits. For example, 16 SIMD processing lanes are used toperform an operation on 512 bits of data.

In some embodiments, two or more SIMD processing lanes operateconcurrently, and in parallel. The number of lanes in an SIMD processor,as well as the number of bits assigned to each lane, can vary withoutlimitation. According to some embodiments, an SIMD processing lane isdefined as having a lane width being any one of 8-bits, 16 bits, 32bits, 64-bits, 128-bits, 256-bits, and 512-bits, without limitation.

In some embodiments, FMA0 to FMA7 of grid of FMAs 210 operate inparallel. In some embodiments, FMA0 to FMA7 of grid of FMAs 210 operateconcurrently.

Here, execution circuit 208 performs FMA instruction 202 on one, 32-bitlane, to generate a 32-bit output. In some embodiments, for example asillustrated and discussed with respect to FIG. 4F, below, executioncircuitry performs the FMA instruction, KVNNI_8_2, on multiple input andweight operands per lane, such as K lanes, producing K intermediate FMAoutputs all accumulating into one final 32-bit output.

In some embodiments, as shown, execution circuit 208 performs the FMAinstruction, VNNI_8_4, by performing multiply-accumulate operationsusing fused multiply-add (FMA) hardware units cascaded in a grid of FMAs210, with each FMA accumulating the product of two inputs with a thirdinput. As shown, grid of FMAs 210 accumulates the previous value of DEST[31:0] with the product of in0 and wt0 in FMA0 (With little-endianencoding, in0 is element [0:7] of first source input [63:0] and wt0 iselement [0:3] of second source input [31:0]). The result of FMA0 is fedinto the accumulation input of FMA1, the result of which is fed intoFMA2, and so on until FMA7 generates the sum to be rounded by round 212circuit and saturated by saturate circuit 214, and then stored intodestination 216. In some embodiments, each of the FMA hardware unitsperforms the rounding by itself. In some embodiments, each of the FMAhardware units checks for saturation and performs the saturating usingsaturation circuit 214. Some embodiments do not include rounding and/orsaturation circuitry.

Accordingly, execution circuit 208, by executing an FMA instruction withasymmetric inputs, with the weight input being less precise and using 4bits instead of 8 bits, improves the processor in which it isincorporated by providing a 2× improvement, or doubling, of FMAinstruction throughput.

FIG. 3 is a block diagram illustrating execution circuitry to process afused multiply-add (FMA) instruction, according to some embodiments. Asshown, FMA instruction 302 is a VNNI_8_4 FMA instruction having fieldsto specify an opcode, a destination, and first and second source vectorshaving first and second widths, respectively. In the context of neuralnetworks, and as shown, the first source vector can represent an inputvector, and the second source vector can represent a weights vector.Here, the identified first source is input [63:0], consisting of eight,8-bit input elements. The identified second source is weights [31:0]vector, consisting of eight, 4-bit weight elements. In some embodiments,one or more of the identified first source, second source, anddestination are stored in registers, such as in a register file of aprocessor, for example as illustrated and discussed below with referenceto FIG. 10. In some embodiments, one or more of the identified firstsource, second source, and destination are stored in a memory location.

In some embodiments, as shown, execution circuit 308 performs the FMAinstruction, VNNI_8_4, using FMA hardware units as illustrated in gridof FMAs 310, which uses FMA hardware units 312A-H to generate the eightproducts specified by Equation 1, above. In some embodiments, FMAhardware units 312A-H operate concurrently and in parallel. Accumulator314 accumulates the previous value of DEST [31:0] with the productsgenerated by 312A-H. The resulting sum is rounded by rounding circuit316 and saturated by saturate circuit 318, and then stored intodestination 320. In some embodiments, each of the FMA hardware unitsperforms the rounding by itself. In some embodiments, each of the FMAhardware units performs the saturating. Some embodiments do not includerounding and/or saturation circuitry.

Accordingly, execution circuit 308, by executing an FMA instruction withasymmetric inputs, with the weight input being less precise and using 4bits instead of 8 bits, improves the processor in which it isincorporated by providing a 2× improvement, or doubling, of FMAinstruction throughput.

FIG. 4A is a block diagram illustrating execution circuitry to process aVNNI_8_4 FMA instruction, according to some embodiments. As shown, FMAinstruction 400, here being VNNI_8_4, identifies first source vector,SRC1 [63:0] 402, having eight, 8-bit input values, second source vector,SRC2 [31:0] 404, having eight, 4-bit weight values, and 32-bitdestination register, DEST [31:0] 412. In operation, execution circuit406 is a 32-bit SIMD processing lane that uses grid of FMAs 408 toaccumulate eight input-weight products with previous contents ofdestination 412. In some embodiments, execution circuit 406 usesrounding and saturation circuit 410 to check for saturation and saturatethe resulting, accumulated sum, and to round the sum to fit into the 32bits of DEST 412. Similar to the execution circuits illustrated anddescribed with respect to FIG. 2 and FIG. 3, execution circuit 406performs Equation 1, above, using a single, 32-bit lane. In someembodiments, execution circuit 406 cascades the FMA hardware units ofgrid of FMAs 408, for example as shown and described with respect toFIG. 2. In some embodiments, execution circuit 406 arranges the FMAhardware units of grid of FMAs 408 in parallel, for example as shown anddescribed with respect to in FIG. 3.

Accordingly, execution circuit 406, by executing an FMA instruction withasymmetric inputs, with the weight input being less precise and using 4bits instead of 8 bits, improves the processor in which it isincorporated by providing a 2× improvement, or doubling, of FMAinstruction throughput.

FIG. 4B is a block diagram illustrating execution circuitry to process aVNNI_8_2 FMA instruction, according to some embodiments. As shown, FMAinstruction 420, here being VNNI_8_2, identifies first source vector,SRC1 [127:0] 422, having sixteen, 8-bit input values, second sourcevector, SRC2 [31:0] 424, having sixteen, 2-bit weight values, and 32-bitdestination register, DEST [31:0] 432. In operation, execution circuit426 uses grid of FMAs 428 to accumulate products of sixteen eight-bitinputs and sixteen corresponding two-bit weights with previous contentsof DEST 432. In some embodiments, execution circuit 426 uses roundingand saturation circuit 430 to check for saturation and saturate theaccumulated sum and to round the sum to fit into the 32 bits of DEST432. Execution circuit 426 performs the VNNI_8_2 FMA instruction 420using a single, 32-bit SIMD processing lane. In some embodiments,execution circuit 426 arranges the sixteen FMA hardware units of grid ofFMAs 428 serially, for example as shown and described with respect toFIG. 2. In some embodiments, execution circuit 426 arranges the FMAhardware units of grid of FMAs 428 in parallel, for example as shown anddescribed with respect to in FIG. 3.

Accordingly, execution circuit 426, by executing an FMA instruction withasymmetric inputs, with the weight input being less precise and using 2bits instead of 8 bits, improves the processor in which it isincorporated by providing a 4× improvement, or quadrupling, of FMAinstruction throughput.

FIG. 4C is a block diagram illustrating execution circuitry to process aVNNI_8_1 FMA instruction, according to some embodiments. As shown, FMAinstruction 440, here being VNNI_8_1, identifies first source vector,SRC1 [255:0] 442, having thirty-two, 8-bit input values, second sourcevector, SRC2 [31:0] 444, having thirty-two, 1-bit weight values, and32-bit destination register, DEST [31:0] 452. In operation, executioncircuit 446 uses grid of FMAs 448 to accumulate products of thirty-twoeight-bit inputs and thirty-two one-bit weights with previous contentsof DEST 452. In some embodiments, execution circuit 446 uses roundingand saturation circuit 450 to check for saturation and saturate theaccumulated sum and to round the sum to fit into the 32 bits of DEST452. Execution circuit 426 performs the VNNI_8_1 FMA instruction 440using a single, 32-bit SIMD processing lane. In some embodiments,execution circuit 446 arranges the thirty-two FMA hardware units of gridof FMAs 448 serially, for example as shown and described with respect toFIG. 2. In some embodiments, execution circuit 446 arranges the FMAhardware units of grid of FMAs 448 in parallel, for example as shown anddescribed with respect to in FIG. 3.

Accordingly, execution circuit 446, by executing an FMA instruction withasymmetric inputs, with the weight input being less precise and using 1bit instead of 8 bits, improves the processor in which it is incorporateby providing an 8× improvement of FMA instruction throughput.

FIG. 4D is a block diagram illustrating execution circuitry to process aVNNI_4_2 FMA instruction, according to some embodiments. As shown, FMAinstruction 460, here being VNNI_4_2, identifies first source vector,SRC1 [63:0] 462, having sixteen, 4-bit input values, second sourcevector, SRC2 [31:0] 464, having sixteen, 2-bit weight values, and 32-bitdestination register, DEST [31:0] 472. In operation, execution circuit466 uses grid of FMAs 468 to accumulate products of sixteen four-bitinputs and sixteen two-bit weights with previous contents of DEST 472.In some embodiments, execution circuit 466 uses rounding and saturationcircuit 470 to check for saturation and saturate the accumulated sum andto round the sum to fit into the 32 bits of DEST 472. Execution circuit466 performs the VNNI_4_2 FMA instruction 460 using a single, 32-bitSIMD processing lane. In some embodiments, execution circuit 466arranges the sixteen FMA hardware units of grid of FMAs 468 serially,for example as shown and described with respect to FIG. 2. In someembodiments, execution circuit 466 arranges the sixteen FMA hardwareunits of grid of FMAs 468 in parallel, for example as shown anddescribed with respect to in FIG. 3.

Accordingly, execution circuit 466, by executing an FMA instruction withasymmetric inputs, with the first source, input vector being lessprecise and using 4 bits instead of 8 bits, and the weight input alsobeing less precise and using 2 bits instead of 8 bits, improves theprocessor in which it is incorporate by providing an 8× improvement ofFMA instruction throughput.

FIG. 4E is a block diagram illustrating execution circuitry to process aVNNI_4_1 FMA instruction, according to some embodiments. As shown, FMAinstruction 480, here being VNNI_4_1, identifies first source vector,SRC1 [127:0] 482, having thirty-two, 4-bit input values, second sourcevector, SRC2 [31:0] 484, having thirty-two, 1-bit weight values, and32-bit destination register, DEST [31:0] 492. In operation, executioncircuit 486 uses grid of FMAs 488 to accumulate products of thirty-twofour-bit inputs and thirty-two corresponding one-bit weights withprevious contents of DEST 492. In some embodiments, execution circuit486 uses rounding and saturation circuit 490 to check for saturation andsaturate the accumulated sum and to round the sum to fit into the 32bits of DEST 492. Execution circuit 486 performs the VNNI_4_1 FMAinstruction 480 using a single, 32-bit SIMD processing lane. In someembodiments, execution circuit 486 arranges the thirty-two FMA hardwareunits of grid of FMAs 488 serially, for example as shown and describedwith respect to FIG. 2. In some embodiments, execution circuit 486arranges the thirty-two FMA hardware units of grid of FMAs 488 inparallel, for example as shown and described with respect to in FIG. 3.

Accordingly, execution circuit 486, by executing an FMA instruction withasymmetric inputs, with the first source, input vector being lessprecise and using 4 bits instead of 8 bits, and the weight input alsobeing less precise and using 1 bit instead of 8 bits, improves theprocessor in which it is incorporate by providing a 16× improvement ofFMA instruction throughput.

It should be noted that, as illustrated in FIGS. 4A-E, a single, 32-bitSIMD processing lane can be used to execute the FMA instruction, whetherthe size of the input elements is four bits or eight bits, and whetherthe size of the weights is 1 bit, 2 bits, or 4 bits. In other words, asingle, 32-bit SIMD processing lane can be used to execute any ofVNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1.

It should be noted that, as illustrated in FIGS. 4A-E, a single, 32-bitSIMD processing lane is used to generate a single, 32-bit destinationfor any of VNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMAinstructions. Different lanes sizes can be used, still in keeping withthe teachings of embodiments disclosed herein. For example, a 16-bitlane could be used to execute any of the VNNI_8_4, wherein the executioncircuitry would multiply four 8-bit inputs by four four-bit weights inparallel. Similarly, a 16-bit lane could be used to execute an of theVNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMA instructions, albeit witheight, sixteen, eight, and sixteen parallel multiplications of inputsand weights, respectively. As another example, the VNNI_8_4, VNNI_8_2,VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMA instructions in some embodimentsare executed using 64-bit SIMD processing lanes, performing 16, 32, 64,32, and 64 multiplications of inputs and weights, respectively. Asanother example, the VNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, andVNNI_4_1 FMA instructions in some embodiments are executed using 128-bitSIMD processing lanes, performing 32, 64, 128, 64, and 128multiplications of inputs and weights, respectively.

FIG. 4F is a block diagram illustrating execution circuitry to process aK-way FMA instruction, according to some embodiments. As shown, K-wayFMA instruction 494, here being KVNNI_8_2 FMA, identifies K first sourcevectors, SRC1 [K][127:0] 495, each being an inputs vector consisting of128 bits and storing 16, eight-bit values. K-way KVNNI_8_2 FMAinstruction 494 also identifies K second source vectors, SRC2 [K][31:0]496, each being a weights vector being 32-bits wide and having sixteen,2-bit weight values. K-way KVNNI_8_2 FMA instruction 494 also identifiesN 32-bit destination registers, DEST [N][31:0] 499. In operation,execution circuit 497 uses K FMA circuits 498 to, for each of the Kintermediate outputs, accumulate products of sixteen eight-bit inputsand sixteen corresponding two-bit weights with previous contents of thecorresponding destination output. In operation, each SIMD processinglane n operates on the identified SRC1, or inputs[k][127:0], and theidentified SRC2, or weights[k][31:0], to generate a result to be writtento the identified DEST [n][31:0], or output[31:0].

In some embodiments, the FMA instruction includes a repeat indicator,either as a separate field, or as part of the opcode. For example, aletter “Q” can be added to the opcode to indicate that the executioncircuitry is to use four SIMD processing lanes to generate fourdestinations. For example, a letter “D” can be added to the opcode toindicate that the execution circuitry is to use two input operands tocompute FMA and accumulate into one destinations. For example, a prefix“OCTA” can be added to the opcode to indicate that the executioncircuitry is to use eight input operands to compute FMA and accumulateinto one destination. FIG. 7 and FIGS. 9A-D, below, disclose furtherdescriptions of the FMA instruction format.

FIG. 5 is pseudocode illustrating execution circuitry to processVNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMA instructions,according to some embodiments. The pseudocode for processing theVNNI_8_4, VNNI_8_2, VNNI_8_1, VNNI_4_2, and VNNI_4_1 FMA instructions isfurther illustrated and described with respect to FIGS. 4A-4E,respectively.

FIG. 6 is a process flow diagram illustrating execution of an FMAinstruction by a processor, according to some embodiments. At 602, theprocessor fetches, by fetch circuitry, an FMA instruction having fieldsto specify an opcode, a destination, and first and second source vectorshaving first and second widths, respectively. The FMA instructionfetched at 602 may be referred to as an asymmetric FMA instruction,insofar as its inputs may have different widths, or precision levels. At604, the processor decodes, by decode circuitry, the fetched FMAinstruction. At 606, the processor optionally schedules execution of thedecoded FMA instruction by an SIMD execution circuit. Operation 606 isoptional, as indicated by its dashed border, insofar as schedulingexecution of the decoded instruction may occur at a different time, ornot at all. At 608, the processor executes, by a single instructionmultiple data (SIMD) execution circuit, the decoded FMA instruction byprocessing as many elements of the second source vector as fit into aSIMD lane width by multiplying each element by a corresponding elementof the first source vector, and accumulating a resulting product withprevious contents of the destination; wherein the SIMD lane width is oneof 16 bits, 32 bits, and 64 bits, the first width is one of 4 bits and 8bits, and the second width is one of 1 bit, 2 bits, and 4 bits. At 610,the processor optionally commits or retires the executed FMAinstruction. Operation 610 is optional, as indicated by its dashedborder, insofar as it may occur at a different time, or not at all.

FIG. 7 is an exemplary format of an FMA instruction, according to someembodiments. As shown, FMA instruction 700 includes opcode 702, DSTidentifier 704, SRC1 identifier 706, SRC2 identifier 708, weight size710, input size 712, and repeat indicator 714. Opcode 702 is shown asVNNI*, which includes an asterisk to indicate that it may optionallyinclude additional prefixes or suffixes to specify additionalinstruction behaviors. For example, opcode 702 may include an input sizeof 8 or 4, and a weight size of 4 or 2 or 1, as illustrated in exemplaryFIG. 4A-FIG. 4F. Opcode 702 may optionally include a prefix, such as“OCT,” or “Q” or “D,” to serve as a repeat indicator of eight, four, ortwo, respectively. The format of the FMA instruction is furtherillustrated and described below with respect to FIG. 8A, FIG. 8B, andFIGS. 9A-D.

Instruction Sets

An instruction set may include one or more instruction formats. A giveninstruction format may define various fields (e.g., number of bits,location of bits) to specify, among other things, the operation to beperformed (e.g., opcode) and the operand(s) on which that operation isto be performed and/or other data field(s) (e.g., mask). Someinstruction formats are further broken down though the definition ofinstruction templates (or subformats). For example, the instructiontemplates of a given instruction format may be defined to have differentsubsets of the instruction format's fields (the included fields aretypically in the same order, but at least some have different bitpositions because there are less fields included) and/or defined to havea given field interpreted differently. Thus, each instruction of an ISAis expressed using a given instruction format (and, if defined, in eachone of the instruction templates of that instruction format) andincludes fields for specifying the operation and the operands. Forexample, an exemplary ADD instruction has a specific opcode and aninstruction format that includes an opcode field to specify that opcodeand operand fields to select operands (source1/destination and source2);and an occurrence of this ADD instruction in an instruction stream willhave specific contents in the operand fields that select specificoperands. A set of SIMD extensions referred to as the Advanced VectorExtensions (AVX) (AVX1 and AVX2) and using the Vector Extensions (VEX)coding scheme has been released and/or published (e.g., see Intel® 64and IA-32 Architectures Software Developer's Manual, September 2014; andsee Intel® Advanced Vector Extensions Programming Reference, October2014).

Exemplary Instruction Formats

Embodiments of the instruction(s) described herein may be embodied indifferent formats. Additionally, exemplary systems, architectures, andpipelines are detailed below. Embodiments of the instruction(s) may beexecuted on such systems, architectures, and pipelines, but are notlimited to those detailed.

Generic Vector Friendly Instruction Format

A vector friendly instruction format is an instruction format that issuited for vector instructions (e.g., there are certain fields specificto vector operations). While embodiments are described in which bothvector and scalar operations are supported through the vector friendlyinstruction format, alternative embodiments use only vector operationsthe vector friendly instruction format.

FIGS. 8A-8B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according toembodiments of the invention. FIG. 8A is a block diagram illustrating ageneric vector friendly instruction format and class A instructiontemplates thereof according to embodiments of the invention; while FIG.8B is a block diagram illustrating the generic vector friendlyinstruction format and class B instruction templates thereof accordingto embodiments of the invention. Specifically, a generic vector friendlyinstruction format 800 for which are defined class A and class Binstruction templates, both of which include no memory access 805instruction templates and memory access 820 instruction templates. Theterm generic in the context of the vector friendly instruction formatrefers to the instruction format not being tied to any specificinstruction set.

While embodiments of the invention will be described in which the vectorfriendly instruction format supports the following: a 64 byte vectoroperand length (or size) with 32 bit (4 byte) or 64 bit (8 byte) dataelement widths (or sizes) (and thus, a 64 byte vector consists of either16 doubleword-size elements or alternatively, 8 quadword-size elements);a 64 byte vector operand length (or size) with 16 bit (2 byte) or 8 bit(1 byte) data element widths (or sizes); a 32 byte vector operand length(or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit (2 byte), or 8bit (1 byte) data element widths (or sizes); and a 16 byte vectoroperand length (or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit(2 byte), or 8 bit (1 byte) data element widths (or sizes); alternativeembodiments may support more, less and/or different vector operand sizes(e.g., 256 byte vector operands) with more, less, or different dataelement widths (e.g., 128 bit (16 byte) data element widths).

The class A instruction templates in FIG. 8A include: 1) within the nomemory access 805 instruction templates there is shown a no memoryaccess, full round control type operation 810 instruction template and ano memory access, data transform type operation 815 instructiontemplate; and 2) within the memory access 820 instruction templatesthere is shown a memory access, temporal 825 instruction template and amemory access, non-temporal 830 instruction template. The class Binstruction templates in FIG. 8B include: 1) within the no memory access805 instruction templates there is shown a no memory access, write maskcontrol, partial round control type operation 812 instruction templateand a no memory access, write mask control type operation 817instruction template; and 2) within the memory access 820 instructiontemplates there is shown a memory access, write mask control 827instruction template.

The generic vector friendly instruction format 800 includes thefollowing fields listed below in the order illustrated in FIGS. 8A-8B.

Format field 840—a specific value (an instruction format identifiervalue) in this field uniquely identifies the vector friendly instructionformat, and thus occurrences of instructions in the vector friendlyinstruction format in instruction streams. As such, this field isoptional in the sense that it is not needed for an instruction set thathas only the generic vector friendly instruction format.

Base operation field 842—its content distinguishes different baseoperations.

Register index field 844—its content, directly or through addressgeneration, specifies the locations of the source and destinationoperands, be they in registers or in memory. These include a sufficientnumber of bits to select N registers from a P×Q (e.g. 32×512, 16×128,32×1024, 64×1024) register file. While in one embodiment N may be up tothree sources and one destination register, alternative embodiments maysupport more or less sources and destination registers (e.g., maysupport up to two sources where one of these sources also acts as thedestination, may support up to three sources where one of these sourcesalso acts as the destination, may support up to two sources and onedestination).

Modifier field 846—its content distinguishes occurrences of instructionsin the generic vector instruction format that specify memory access fromthose that do not; that is, between no memory access 805 instructiontemplates and memory access 820 instruction templates. Memory accessoperations read and/or write to the memory hierarchy (in some casesspecifying the source and/or destination addresses using values inregisters), while non-memory access operations do not (e.g., the sourceand destinations are registers). While in one embodiment this field alsoselects between three different ways to perform memory addresscalculations, alternative embodiments may support more, less, ordifferent ways to perform memory address calculations.

Augmentation operation field 850—its content distinguishes which one ofa variety of different operations to be performed in addition to thebase operation. This field is context specific. In one embodiment of theinvention, this field is divided into a class field 868, an alpha field852, and a beta field 854. The augmentation operation field 850 allowscommon groups of operations to be performed in a single instructionrather than 2, 3, or 4 instructions.

Scale field 860—its content allows for the scaling of the index field'scontent for memory address generation (e.g., for address generation thatuses 2^(scale)*index+base).

Displacement Field 862A—its content is used as part of memory addressgeneration (e.g., for address generation that uses2^(scale)*index+base+displacement).

Displacement Factor Field 862B (note that the juxtaposition ofdisplacement field 862A directly over displacement factor field 862Bindicates one or the other is used)—its content is used as part ofaddress generation; it specifies a displacement factor that is to bescaled by the size of a memory access (N)—where N is the number of bytesin the memory access (e.g., for address generation that uses2^(scale)*index+base+scaled displacement). Redundant low-order bits areignored and hence, the displacement factor field's content is multipliedby the memory operands total size (N) in order to generate the finaldisplacement to be used in calculating an effective address. The valueof N is determined by the processor hardware at runtime based on thefull opcode field 874 (described later herein) and the data manipulationfield 854C. The displacement field 862A and the displacement factorfield 862B are optional in the sense that they are not used for the nomemory access 805 instruction templates and/or different embodiments mayimplement only one or none of the two.

Data element width field 864—its content distinguishes which one of anumber of data element widths is to be used (in some embodiments for allinstructions; in other embodiments for only some of the instructions).This field is optional in the sense that it is not needed if only onedata element width is supported and/or data element widths are supportedusing some aspect of the opcodes.

Write mask field 870—its content controls, on a per data elementposition basis, whether that data element position in the destinationvector operand reflects the result of the base operation andaugmentation operation. Class A instruction templates supportmerging-write masking, while class B instruction templates support bothmerging- and zeroing-write masking. When merging, vector masks allow anyset of elements in the destination to be protected from updates duringthe execution of any operation (specified by the base operation and theaugmentation operation); in other one embodiment, preserving the oldvalue of each element of the destination where the corresponding maskbit has a 0. In contrast, when zeroing vector masks allow any set ofelements in the destination to be zeroed during the execution of anyoperation (specified by the base operation and the augmentationoperation); in one embodiment, an element of the destination is set to 0when the corresponding mask bit has a 0 value. A subset of thisfunctionality is the ability to control the vector length of theoperation being performed (that is, the span of elements being modified,from the first to the last one); however, it is not necessary that theelements that are modified be consecutive. Thus, the write mask field870 allows for partial vector operations, including loads, stores,arithmetic, logical, etc. While embodiments of the invention aredescribed in which the write mask field's 870 content selects one of anumber of write mask registers that contains the write mask to be used(and thus the write mask field's 870 content indirectly identifies thatmasking to be performed), alternative embodiments instead or additionalallow the mask write field's 870 content to directly specify the maskingto be performed.

Immediate field 872—its content allows for the specification of animmediate. This field is optional in the sense that it is not present inan implementation of the generic vector friendly format that does notsupport immediate and it is not present in instructions that do not usean immediate.

Class field 868—its content distinguishes between different classes ofinstructions. With reference to FIGS. 8A-B, the contents of this fieldselect between class A and class B instructions. In FIGS. 8A-B, roundedcorner squares are used to indicate a specific value is present in afield (e.g., class A 868A and class B 868B for the class field 868respectively in FIGS. 8A-B).

Instruction Templates of Class A

In the case of the non-memory access 805 instruction templates of classA, the alpha field 852 is interpreted as an RS field 852A, whose contentdistinguishes which one of the different augmentation operation typesare to be performed (e.g., round 852A.1 and data transform 852A.2 arerespectively specified for the no memory access, round type operation810 and the no memory access, data transform type operation 815instruction templates), while the beta field 854 distinguishes which ofthe operations of the specified type is to be performed. In the nomemory access 805 instruction templates, the scale field 860, thedisplacement field 862A, and the displacement scale filed 862B are notpresent.

No-Memory Access Instruction Templates—Full Round Control Type Operation

In the no memory access full round control type operation 810instruction template, the beta field 854 is interpreted as a roundcontrol field 854A, whose content(s) provide static rounding. While inthe described embodiments of the invention the round control field 854Aincludes a suppress all floating point exceptions (SAE) field 856 and around operation control field 858, alternative embodiments may supportmay encode both these concepts into the same field or only have one orthe other of these concepts/fields (e.g., may have only the roundoperation control field 858).

SAE field 856—its content distinguishes whether or not to disable theexception event reporting; when the SAE field's 856 content indicatessuppression is enabled, a given instruction does not report any kind offloating-point exception flag and does not raise any floating pointexception handler.

Round operation control field 858—its content distinguishes which one ofa group of rounding operations to perform (e.g., Round-up, Round-down,Round-towards-zero and Round-to-nearest). Thus, the round operationcontrol field 858 allows for the changing of the rounding mode on a perinstruction basis. In one embodiment of the invention where a processorincludes a control register for specifying rounding modes, the roundoperation control field's 850 content overrides that register value.

No Memory Access Instruction Templates—Data Transform Type Operation

In the no memory access data transform type operation 815 instructiontemplate, the beta field 854 is interpreted as a data transform field854B, whose content distinguishes which one of a number of datatransforms is to be performed (e.g., no data transform, swizzle,broadcast).

In the case of a memory access 820 instruction template of class A, thealpha field 852 is interpreted as an eviction hint field 852B, whosecontent distinguishes which one of the eviction hints is to be used (inFIG. 8A, temporal 852B.1 and non-temporal 852B.2 are respectivelyspecified for the memory access, temporal 825 instruction template andthe memory access, non-temporal 830 instruction template), while thebeta field 854 is interpreted as a data manipulation field 854C, whosecontent distinguishes which one of a number of data manipulationoperations (also known as primitives) is to be performed (e.g., nomanipulation; broadcast; up conversion of a source; and down conversionof a destination). The memory access 820 instruction templates includethe scale field 860, and optionally the displacement field 862A or thedisplacement scale field 862B.

Vector memory instructions perform vector loads from and vector storesto memory, with conversion support. As with regular vector instructions,vector memory instructions transfer data from/to memory in a dataelement-wise fashion, with the elements that are actually transferred isdictated by the contents of the vector mask that is selected as thewrite mask.

Memory Access Instruction Templates—Temporal

Temporal data is data likely to be reused soon enough to benefit fromcaching. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Memory Access Instruction Templates—Non-Temporal

Non-temporal data is data unlikely to be reused soon enough to benefitfrom caching in the 1st-level cache and should be given priority foreviction. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Instruction Templates of Class B

In the case of the instruction templates of class B, the alpha field 852is interpreted as a write mask control (Z) field 852C, whose contentdistinguishes whether the write masking controlled by the write maskfield 870 should be a merging or a zeroing.

In the case of the non-memory access 805 instruction templates of classB, part of the beta field 854 is interpreted as an RL field 857A, whosecontent distinguishes which one of the different augmentation operationtypes are to be performed (e.g., round 857A.1 and vector length (VSIZE)857A.2 are respectively specified for the no memory access, write maskcontrol, partial round control type operation 812 instruction templateand the no memory access, write mask control, VSIZE type operation 817instruction template), while the rest of the beta field 854distinguishes which of the operations of the specified type is to beperformed. In the no memory access 805 instruction templates, the scalefield 860, the displacement field 862A, and the displacement scale filed862B are not present.

In the no memory access, write mask control, partial round control typeoperation 810 instruction template, the rest of the beta field 854 isinterpreted as a round operation field 859A and exception eventreporting is disabled (a given instruction does not report any kind offloating-point exception flag and does not raise any floating pointexception handler).

Round operation control field 859A—just as round operation control field858, its content distinguishes which one of a group of roundingoperations to perform (e.g., Round-up, Round-down, Round-towards-zeroand Round-to-nearest). Thus, the round operation control field 859Aallows for the changing of the rounding mode on a per instruction basis.In one embodiment of the invention where a processor includes a controlregister for specifying rounding modes, the round operation controlfield's 850 content overrides that register value.

In the no memory access, write mask control, VSIZE type operation 817instruction template, the rest of the beta field 854 is interpreted as avector length field 859B, whose content distinguishes which one of anumber of data vector lengths is to be performed on (e.g., 128, 256, or512 byte).

In the case of a memory access 820 instruction template of class B, partof the beta field 854 is interpreted as a broadcast field 857B, whosecontent distinguishes whether or not the broadcast type datamanipulation operation is to be performed, while the rest of the betafield 854 is interpreted the vector length field 859B. The memory access820 instruction templates include the scale field 860, and optionallythe displacement field 862A or the displacement scale field 862B.

With regard to the generic vector friendly instruction format 800, afull opcode field 874 is shown including the format field 840, the baseoperation field 842, and the data element width field 864. While oneembodiment is shown where the full opcode field 874 includes all ofthese fields, the full opcode field 874 includes less than all of thesefields in embodiments that do not support all of them. The full opcodefield 874 provides the operation code (opcode).

The augmentation operation field 850, the data element width field 864,and the write mask field 870 allow these features to be specified on aper instruction basis in the generic vector friendly instruction format.

The combination of write mask field and data element width field createtyped instructions in that they allow the mask to be applied based ondifferent data element widths.

The various instruction templates found within class A and class B arebeneficial in different situations. In some embodiments of theinvention, different processors or different cores within a processormay support only class A, only class B, or both classes. For instance, ahigh performance general purpose out-of-order core intended forgeneral-purpose computing may support only class B, a core intendedprimarily for graphics and/or scientific (throughput) computing maysupport only class A, and a core intended for both may support both (ofcourse, a core that has some mix of templates and instructions from bothclasses but not all templates and instructions from both classes iswithin the purview of the invention). Also, a single processor mayinclude multiple cores, all of which support the same class or in whichdifferent cores support different class. For instance, in a processorwith separate graphics and general purpose cores, one of the graphicscores intended primarily for graphics and/or scientific computing maysupport only class A, while one or more of the general purpose cores maybe high performance general purpose cores with out of order executionand register renaming intended for general-purpose computing thatsupport only class B. Another processor that does not have a separategraphics core, may include one more general purpose in-order orout-of-order cores that support both class A and class B. Of course,features from one class may also be implement in the other class indifferent embodiments of the invention. Programs written in a high levellanguage would be put (e.g., just in time compiled or staticallycompiled) into an variety of different executable forms, including: 1) aform having only instructions of the class(es) supported by the targetprocessor for execution; or 2) a form having alternative routineswritten using different combinations of the instructions of all classesand having control flow code that selects the routines to execute basedon the instructions supported by the processor which is currentlyexecuting the code.

Exemplary Specific Vector Friendly Instruction Format

FIG. 9A is a block diagram illustrating an exemplary specific vectorfriendly instruction format according to embodiments of the invention.FIG. 9A shows a specific vector friendly instruction format 900 that isspecific in the sense that it specifies the location, size,interpretation, and order of the fields, as well as values for some ofthose fields. The specific vector friendly instruction format 900 may beused to extend the x86 instruction set, and thus some of the fields aresimilar or the same as those used in the existing x86 instruction setand extension thereof (e.g., AVX). This format remains consistent withthe prefix encoding field, real opcode byte field, MOD RIM field, SIBfield, displacement field, and immediate fields of the existing x86instruction set with extensions. The fields from FIGS. 8A-B into whichthe fields from FIG. 9A map are illustrated.

Although embodiments of the invention are described with reference tothe specific vector friendly instruction format 900 in the context ofthe generic vector friendly instruction format 800 for illustrativepurposes, the invention is not limited to the specific vector friendlyinstruction format 900 except where claimed. For example, the genericvector friendly instruction format 800 contemplates a variety ofpossible sizes for the various fields, while the specific vectorfriendly instruction format 900 is shown as having fields of specificsizes. By way of specific example, while the data element width field864 is illustrated as a one bit field in the specific vector friendlyinstruction format 900, the invention is not so limited (that is, thegeneric vector friendly instruction format 800 contemplates other sizesof the data element width field 864).

The generic vector friendly instruction format 800 includes thefollowing fields listed below in the order illustrated in FIG. 9A.

EVEX Prefix (Bytes 0-3) 902—is encoded in a four-byte form.

Format Field 840 (EVEX Byte 0, bits [7:0])—the first byte (EVEX Byte 0)is the format field 840 and it contains 0x62 (the unique value used fordistinguishing the vector friendly instruction format in one embodimentof the invention).

The second-fourth bytes (EVEX Bytes 1-3) include many bit fieldsproviding specific capability.

REX field 905 (EVEX Byte 1, bits [7-5])—consists of an EVEX.R bit field(EVEX Byte 1, bit [7]-R), EVEX.X bit field (EVEX byte 1, bit [6]-X), and857BEX byte 1, bit[5]-B). The EVEX.R, EVEX.X, and EVEX.B bit fieldsprovide the same functionality as the corresponding VEX bit fields, andare encoded using 1s complement form, i.e. ZMM0 is encoded as 1111B,ZMM15 is encoded as 0000B. Other fields of the instructions encode thelower three bits of the register indexes as is known in the art (rrr,xxx, and bbb), so that Rrrr, Xxxx, and Bbbb may be formed by addingEVEX.R, EVEX.X, and EVEX.B.

REX′ field 810—this is the first part of the REX′ field 810 and is theEVEX.R′ bit field (EVEX Byte 1, bit [4]-R′) that is used to encodeeither the upper 16 or lower 16 of the extended 32 register set. In oneembodiment of the invention, this bit, along with others as indicatedbelow, is stored in bit inverted format to distinguish (in thewell-known x86 32-bit mode) from the BOUND instruction, whose realopcode byte is 62, but does not accept in the MOD R/M field (describedbelow) the value of 11 in the MOD field; alternative embodiments of theinvention do not store this and the other indicated bits below in theinverted format. A value of 1 is used to encode the lower 16 registers.In other words, R′Rrrr is formed by combining EVEX.R′, EVEX.R, and theother RRR from other fields.

Opcode map field 915 (EVEX byte 1, bits [3:0]-mmmm)—its content encodesan implied leading opcode byte (0F, 0F 38, or 0F 3).

Data element width field 864 (EVEX byte 2, bit [7]-W)—is represented bythe notation EVEX.W. EVEX.W is used to define the granularity (size) ofthe datatype (either 32-bit data elements or 64-bit data elements).

EVEX.vvvv 920 (EVEX Byte 2, bits [6:3]-vvvv)—the role of EVEX.vvvv mayinclude the following: 1) EVEX.vvvv encodes the first source registeroperand, specified in inverted (1s complement) form and is valid forinstructions with 2 or more source operands; 2) EVEX.vvvv encodes thedestination register operand, specified in 1s complement form forcertain vector shifts; or 3) EVEX.vvvv does not encode any operand, thefield is reserved and should contain 1111b. Thus, EVEX.vvvv field 920encodes the 4 low-order bits of the first source register specifierstored in inverted (1s complement) form. Depending on the instruction,an extra different EVEX bit field is used to extend the specifier sizeto 32 registers.

EVEX.U 868 Class field (EVEX byte 2, bit [2]-U)—If EVEX.U=0, itindicates class A or EVEX.U0; if EVEX.U=1, it indicates class B orEVEX.U1.

Prefix encoding field 925 (EVEX byte 2, bits [1:0]-pp)—providesadditional bits for the base operation field. In addition to providingsupport for the legacy SSE instructions in the EVEX prefix format, thisalso has the benefit of compacting the SIMD prefix (rather thanrequiring a byte to express the SIMD prefix, the EVEX prefix requiresonly 2 bits). In one embodiment, to support legacy SSE instructions thatuse an SIMD prefix (66H, F2H, F3H) in both the legacy format and in theEVEX prefix format, these legacy SIMD prefixes are encoded into the SIMDprefix encoding field; and at runtime are expanded into the legacy SIMDprefix prior to being provided to the decoder's PLA (so the PLA canexecute both the legacy and EVEX format of these legacy instructionswithout modification). Although newer instructions could use the EVEXprefix encoding field's content directly as an opcode extension, certainembodiments expand in a similar fashion for consistency but allow fordifferent meanings to be specified by these legacy SIMD prefixes. Analternative embodiment may redesign the PLA to support the 2 bit SIMDprefix encodings, and thus not require the expansion.

Alpha field 852 (EVEX byte 3, bit [7]-EH; also known as EVEX.EH,EVEX.rs, EVEX.RL, EVEX.write mask control, and EVEX.N; also illustratedwith α)—as previously described, this field is context specific.

Beta field 854 (EVEX byte 3, bits [6:4]-SSS, also known as EVEX.s₂₋₀,EVEX.r₂₋₀, EVEX.rr1, EVEX.LL0, EVEX.LLB; also illustrated with βββ)—aspreviously described, this field is context specific.

REX′ field 810—this is the remainder of the REX′ field and is theEVEX.V′ bit field (EVEX Byte 3, bit [3]-V′) that may be used to encodeeither the upper 16 or lower 16 of the extended 32 register set. Thisbit is stored in bit inverted format. A value of 1 is used to encode thelower 16 registers. In other words, V′VVVV is formed by combiningEVEX.V′, EVEX.vvvv.

Write mask field 870 (EVEX byte 3, bits [2:0]-kkk)—its content specifiesthe index of a register in the write mask registers as previouslydescribed. In one embodiment of the invention, the specific valueEVEX.kkk=000 has a special behavior implying no write mask is used forthe particular instruction (this may be implemented in a variety of waysincluding the use of a write mask hardwired to all ones or hardware thatbypasses the masking hardware).

Real Opcode Field 930 (Byte 4) is also known as the opcode byte. Part ofthe opcode is specified in this field.

MOD R/M Field 940 (Byte 5) includes MOD field 942, Reg field 944, andR/M field 946. As previously described, the MOD field's 942 contentdistinguishes between memory access and non-memory access operations.The role of Reg field 944 can be summarized to two situations: encodingeither the destination register operand or a source register operand, orbe treated as an opcode extension and not used to encode any instructionoperand. The role of RIM field 946 may include the following: encodingthe instruction operand that references a memory address, or encodingeither the destination register operand or a source register operand.

Scale, Index, Base (SIB) Byte (Byte 6)—As previously described, thescale field's 850 content is used for memory address generation. SIB.xxx954 and SIB.bbb 956—the contents of these fields have been previouslyreferred to with regard to the register indexes Xxxx and Bbbb.

Displacement field 862A (Bytes 7-10)—when MOD field 942 contains 10,bytes 7-10 are the displacement field 862A, and it works the same as thelegacy 32-bit displacement (disp32) and works at byte granularity.

Displacement factor field 862B (Byte 7)—when MOD field 942 contains 01,byte 7 is the displacement factor field 862B. The location of this fieldis that same as that of the legacy x86 instruction set 8-bitdisplacement (disp8), which works at byte granularity. Since disp8 issign extended, it can only address between −128 and 127 bytes offsets;in terms of 64 byte cache lines, disp8 uses 8 bits that can be set toonly four really useful values −128, −64, 0, and 64; since a greaterrange is often needed, disp32 is used; however, disp32 requires 4 bytes.In contrast to disp8 and disp32, the displacement factor field 862B is areinterpretation of disp8; when using displacement factor field 862B,the actual displacement is determined by the content of the displacementfactor field multiplied by the size of the memory operand access (N).This type of displacement is referred to as disp8*N. This reduces theaverage instruction length (a single byte of used for the displacementbut with a much greater range). Such compressed displacement is based onthe assumption that the effective displacement is multiple of thegranularity of the memory access, and hence, the redundant low-orderbits of the address offset do not need to be encoded. In other words,the displacement factor field 862B substitutes the legacy x86instruction set 8-bit displacement. Thus, the displacement factor field862B is encoded the same way as an x86 instruction set 8-bitdisplacement (so no changes in the ModRM/SIB encoding rules) with theonly exception that disp8 is overloaded to disp8*N. In other words,there are no changes in the encoding rules or encoding lengths but onlyin the interpretation of the displacement value by hardware (which needsto scale the displacement by the size of the memory operand to obtain abyte-wise address offset). Immediate field 872 operates as previouslydescribed.

Full Opcode Field

FIG. 9B is a block diagram illustrating the fields of the specificvector friendly instruction format 900 that make up the full opcodefield 874 according to one embodiment of the invention. Specifically,the full opcode field 874 includes the format field 840, the baseoperation field 842, and the data element width (W) field 864. The baseoperation field 842 includes the prefix encoding field 925, the opcodemap field 915, and the real opcode field 930.

Register Index Field

FIG. 9C is a block diagram illustrating the fields of the specificvector friendly instruction format 900 that make up the register indexfield 844 according to one embodiment of the invention. Specifically,the register index field 844 includes the REX field 905, the REX′ field910, the MODR/M.reg field 944, the MODR/M.r/m field 946, the VVVV field920, xxx field 954, and the bbb field 956.

Augmentation Operation Field

FIG. 9D is a block diagram illustrating the fields of the specificvector friendly instruction format that makes up the augmentationoperation field 850 according to one embodiment of the invention. Whenthe class (U) field 868 contains 0, it signifies EVEX.U0 (class A 868A);when it contains 1, it signifies EVEX.U1 (class B 868B). When U=0 andthe MOD field 942 contains 11 (signifying a no memory access operation),the alpha field 852 (EVEX byte 3, bit [7]-EH) is interpreted as the RSfield 852A. When the RS field 852A contains a 1 (round 852A.1), the betafield 854 (EVEX byte 3, bits [6:4]-SSS) is interpreted as the roundcontrol field 854A. The round control field 854A includes a one bit SAEfield 856 and a two bit round operation field 858. When the RS field852A contains a 0 (data transform 852A.2), the beta field 854 (EVEX byte3, bits [6:4]-SSS) is interpreted as a three bit data transform field854B. When U=0 and the MOD field 942 contains 00, 01, or 10 (signifyinga memory access operation), the alpha field 852 (EVEX byte 3, bit[7]-EH) is interpreted as the eviction hint (EH) field 852B and the betafield 854 (EVEX byte 3, bits [6:4]-SSS) is interpreted as a three bitdata manipulation field 854C.

When U=1, the alpha field 852 (EVEX byte 3, bit [7]-EH) is interpretedas the write mask control (Z) field 852C. When U=1 and the MOD field 942contains 11 (signifying a no memory access operation), part of the betafield 854 (EVEX byte 3, bit [4]-S₀) is interpreted as the RL field 857A;when it contains a 1 (round 857A.1) the rest of the beta field 854 (EVEXbyte 3, bit [6-5]-S₂₋₁) is interpreted as the round operation field859A, while when the RL field 857A contains a 0 (VSIZE 857.A2) the restof the beta field 854 (EVEX byte 3, bit [6-5]-S₂₋₁) is interpreted asthe vector length field 859B (EVEX byte 3, bit [6-5]-L₁₋₀). When U=1 andthe MOD field 942 contains 00, 01, or 10 (signifying a memory accessoperation), the beta field 854 (EVEX byte 3, bits [6:4]-SSS) isinterpreted as the vector length field 859B (EVEX byte 3, bit[6-5]-L₁₋₀) and the broadcast field 857B (EVEX byte 3, bit [4]-B).

Exemplary Register Architecture

FIG. 10 is a block diagram of a register architecture 1000 according toone embodiment of the invention. In the embodiment illustrated, thereare 32 vector registers 1010 that are 512 bits wide; these registers arereferenced as zmm0 through zmm31. The lower order 256 bits of the lower16 zmm registers are overlaid on registers ymm0-16. The lower order 128bits of the lower 16 zmm registers (the lower order 128 bits of the ymmregisters) are overlaid on registers xmm0-15. The specific vectorfriendly instruction format 900 operates on these overlaid register fileas illustrated in the below tables.

Adjustable Vector Length Class Operations Registers InstructionTemplates A (FIG. 810, 815, zmm registers (the vector length is that donot include the 8A; U = 0) 825, 830 64 byte) vector length field B (FIG.812 zmm registers (the vector length is 859B 8B; U = 1) 64 byte)Instruction templates B (FIG. 817, 827 zmm, ymm, or xmm registers (thethat do include the 8B; U = 1) vector length is 64-byte, 32 byte, orvector length field 16 byte) depending on the vector 859B length field859B

In other words, the vector length field 859B selects between a maximumlength and one or more other shorter lengths, where each such shorterlength is half the length of the preceding length; and instructionstemplates without the vector length field 859B operate on the maximumvector length. Further, in one embodiment, the class B instructiontemplates of the specific vector friendly instruction format 900 operateon packed or scalar single/double-precision floating point data andpacked or scalar integer data. Scalar operations are operationsperformed on the lowest order data element position in a zmm/ymm/xmmregister; the higher order data element positions are either left thesame as they were prior to the instruction or zeroed depending on theembodiment.

Write mask registers 1015—in the embodiment illustrated, there are 8write mask registers (k0 through k7), each 64 bits in size. In analternate embodiment, the write mask registers 1015 are 16 bits in size.As previously described, in one embodiment of the invention, the vectormask register k0 cannot be used as a write mask; when the encoding thatwould normally indicate k0 is used for a write mask, it selects ahardwired write mask of 0xFFFF, effectively disabling write masking forthat instruction.

General-purpose registers 1025—in the embodiment illustrated, there aresixteen 64-bit general-purpose registers that are used along with theexisting x86 addressing modes to address memory operands. Theseregisters are referenced by the names RAX, RBX, RCX, RDX, RBP, RSI, RDI,RSP, and R8 through R15.

Scalar floating point stack register file (x87 stack) 1045, on which isaliased the MMX packed integer flat register file 1050—in the embodimentillustrated, the x87 stack is an eight-element stack used to performscalar floating-point operations on 32/64/80-bit floating point datausing the x87 instruction set extension; while the MMX registers areused to perform operations on 64-bit packed integer data, as well as tohold operands for some operations performed between the MMX and XMMregisters.

Alternative embodiments of the invention may use wider or narrowerregisters. Additionally, alternative embodiments of the invention mayuse more, less, or different register files and registers.

Exemplary Core Architectures, Processors, and Computer Architectures

Processor cores may be implemented in different ways, for differentpurposes, and in different processors. For instance, implementations ofsuch cores may include: 1) a general purpose in-order core intended forgeneral-purpose computing; 2) a high performance general purposeout-of-order core intended for general-purpose computing; 3) a specialpurpose core intended primarily for graphics and/or scientific(throughput) computing. Implementations of different processors mayinclude: 1) a CPU including one or more general purpose in-order coresintended for general-purpose computing and/or one or more generalpurpose out-of-order cores intended for general-purpose computing; and2) a coprocessor including one or more special purpose cores intendedprimarily for graphics and/or scientific (throughput). Such differentprocessors lead to different computer system architectures, which mayinclude: 1) the coprocessor on a separate chip from the CPU; 2) thecoprocessor on a separate die in the same package as a CPU; 3) thecoprocessor on the same die as a CPU (in which case, such a coprocessoris sometimes referred to as special purpose logic, such as integratedgraphics and/or scientific (throughput) logic, or as special purposecores); and 4) a system on a chip that may include on the same die thedescribed CPU (sometimes referred to as the application core(s) orapplication processor(s)), the above described coprocessor, andadditional functionality. Exemplary core architectures are describednext, followed by descriptions of exemplary processors and computerarchitectures.

Exemplary Core Architectures In-Order and Out-of-Order Core BlockDiagram

FIG. 11A is a block diagram illustrating both an exemplary in-orderpipeline and an exemplary register renaming, out-of-orderissue/execution pipeline according to embodiments of the invention. FIG.11B is a block diagram illustrating both an exemplary embodiment of anin-order architecture core and an exemplary register renaming,out-of-order issue/execution architecture core to be included in aprocessor according to embodiments of the invention. The solid linedboxes in FIGS. 11A-B illustrate the in-order pipeline and in-order core,while the optional addition of the dashed lined boxes illustrates theregister renaming, out-of-order issue/execution pipeline and core. Giventhat the in-order aspect is a subset of the out-of-order aspect, theout-of-order aspect will be described.

In FIG. 11A, a processor pipeline 1100 includes a fetch stage 1102, alength decode stage 1104, a decode stage 1106, an allocation stage 1108,a renaming stage 1110, a scheduling (also known as a dispatch or issue)stage 1112, a register read/memory read stage 1114, an execute stage1116, a write back/memory write stage 1118, an exception handling stage1122, and a commit stage 1124.

FIG. 11B shows processor core 1190 including a front end unit 1130coupled to an execution engine unit 1150, and both are coupled to amemory unit 1170. The core 1190 may be a reduced instruction setcomputing (RISC) core, a complex instruction set computing (CISC) core,a very long instruction word (VLIW) core, or a hybrid or alternativecore type. As yet another option, the core 1190 may be a special-purposecore, such as, for example, a network or communication core, compressionengine, coprocessor core, general purpose computing graphics processingunit (GPGPU) core, graphics core, or the like.

The front end unit 1130 includes a branch prediction unit 1132 coupledto an instruction cache unit 1134, which is coupled to an instructiontranslation lookaside buffer (TLB) 1136, which is coupled to aninstruction fetch unit 1138, which is coupled to a decode unit 1140. Thedecode unit 1140 (or decoder) may decode instructions, and generate asan output one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichare decoded from, or which otherwise reflect, or are derived from, theoriginal instructions. The decode unit 1140 may be implemented usingvarious different mechanisms. Examples of suitable mechanisms include,but are not limited to, look-up tables, hardware implementations,programmable logic arrays (PLAs), microcode read only memories (ROMs),etc. In one embodiment, the core 1190 includes a microcode ROM or othermedium that stores microcode for certain macroinstructions (e.g., indecode unit 1140 or otherwise within the front end unit 1130). Thedecode unit 1140 is coupled to a rename/allocator unit 1152 in theexecution engine unit 1150.

The execution engine unit 1150 includes the rename/allocator unit 1152coupled to a retirement unit 1154 and a set of one or more schedulerunit(s) 1156. The scheduler unit(s) 1156 represents any number ofdifferent schedulers, including reservations stations, centralinstruction window, etc. The scheduler unit(s) 1156 is coupled to thephysical register file(s) unit(s) 1158. Each of the physical registerfile(s) units 1158 represents one or more physical register files,different ones of which store one or more different data types, such asscalar integer, scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point, status (e.g., aninstruction pointer that is the address of the next instruction to beexecuted), etc. In one embodiment, the physical register file(s) unit1158 comprises a vector registers unit, a write mask registers unit, anda scalar registers unit. These register units may provide architecturalvector registers, vector mask registers, and general purpose registers.The physical register file(s) unit(s) 1158 is overlapped by theretirement unit 1154 to illustrate various ways in which registerrenaming and out-of-order execution may be implemented (e.g., using areorder buffer(s) and a retirement register file(s); using a futurefile(s), a history buffer(s), and a retirement register file(s); using aregister maps and a pool of registers; etc.). The retirement unit 1154and the physical register file(s) unit(s) 1158 are coupled to theexecution cluster(s) 1160. The execution cluster(s) 1160 includes a setof one or more execution units 1162 and a set of one or more memoryaccess units 1164. The execution units 1162 may perform variousoperations (e.g., shifts, addition, subtraction, multiplication) and onvarious types of data (e.g., scalar floating point, packed integer,packed floating point, vector integer, vector floating point). Whilesome embodiments may include a number of execution units dedicated tospecific functions or sets of functions, other embodiments may includeonly one execution unit or multiple execution units that all perform allfunctions. The scheduler unit(s) 1156, physical register file(s) unit(s)1158, and execution cluster(s) 1160 are shown as being possibly pluralbecause certain embodiments create separate pipelines for certain typesof data/operations (e.g., a scalar integer pipeline, a scalar floatingpoint/packed integer/packed floating point/vector integer/vectorfloating point pipeline, and/or a memory access pipeline that each havetheir own scheduler unit, physical register file(s) unit, and/orexecution cluster—and in the case of a separate memory access pipeline,certain embodiments are implemented in which only the execution clusterof this pipeline has the memory access unit(s) 1164). It should also beunderstood that where separate pipelines are used, one or more of thesepipelines may be out-of-order issue/execution and the rest in-order.

The set of memory access units 1164 is coupled to the memory unit 1170,which includes a data TLB unit 1172 coupled to a data cache unit 1174coupled to a level 2 (L2) cache unit 1176. In one exemplary embodiment,the memory access units 1164 may include a load unit, a store addressunit, and a store data unit, each of which is coupled to the data TLBunit 1172 in the memory unit 1170. The instruction cache unit 1134 isfurther coupled to a level 2 (L2) cache unit 1176 in the memory unit1170. The L2 cache unit 1176 is coupled to one or more other levels ofcache and eventually to a main memory.

By way of example, the exemplary register renaming, out-of-orderissue/execution core architecture may implement the pipeline 1100 asfollows: 1) the instruction fetch 1138 performs the fetch and lengthdecoding stages 1102 and 1104; 2) the decode unit 1140 performs thedecode stage 1106; 3) the rename/allocator unit 1152 performs theallocation stage 1108 and renaming stage 1110; 4) the scheduler unit(s)1156 performs the schedule stage 1112; 5) the physical register file(s)unit(s) 1158 and the memory unit 1170 perform the register read/memoryread stage 1114; the execution cluster 1160 perform the execute stage1116; 6) the memory unit 1170 and the physical register file(s) unit(s)1158 perform the write back/memory write stage 1118; 7) various unitsmay be involved in the exception handling stage 1122; and 8) theretirement unit 1154 and the physical register file(s) unit(s) 1158perform the commit stage 1124.

The core 1190 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.), including theinstruction(s) described herein. In one embodiment, the core 1190includes logic to support a packed data instruction set extension (e.g.,AVX1, AVX2), thereby allowing the operations used by many multimediaapplications to be performed using packed data.

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor also includes separate instruction and data cache units1134/1174 and a shared L2 cache unit 1176, alternative embodiments mayhave a single internal cache for both instructions and data, such as,for example, a Level 1 (L1) internal cache, or multiple levels ofinternal cache. In some embodiments, the system may include acombination of an internal cache and an external cache that is externalto the core and/or the processor. Alternatively, all of the cache may beexternal to the core and/or the processor.

Specific Exemplary In-Order Core Architecture

FIGS. 12A-B illustrate a block diagram of a more specific exemplaryin-order core architecture, which core would be one of several logicblocks (including other cores of the same type and/or different types)in a chip. The logic blocks communicate through a high-bandwidthinterconnect network (e.g., a ring network) with some fixed functionlogic, memory I/O interfaces, and other necessary I/O logic, dependingon the application.

FIG. 12A is a block diagram of a single processor core, along with itsconnection to the on-die interconnect network 1202 and with its localsubset of the Level 2 (L2) cache 1204, according to embodiments of theinvention. In one embodiment, an instruction decoder 1200 supports thex86 instruction set with a packed data instruction set extension. An L1cache 1206 allows low-latency accesses to cache memory into the scalarand vector units. While in one embodiment (to simplify the design), ascalar unit 1208 and a vector unit 1210 use separate register sets(respectively, scalar registers 1212 and vector registers 1214) and datatransferred between them is written to memory and then read back in froma level 1 (L1) cache 1206, alternative embodiments of the invention mayuse a different approach (e.g., use a single register set or include acommunication path that allow data to be transferred between the tworegister files without being written and read back).

The local subset of the L2 cache 1204 is part of a global L2 cache thatis divided into separate local subsets, one per processor core. Eachprocessor core has a direct access path to its own local subset of theL2 cache 1204. Data read by a processor core is stored in its L2 cachesubset 1204 and can be accessed quickly, in parallel with otherprocessor cores accessing their own local L2 cache subsets. Data writtenby a processor core is stored in its own L2 cache subset 1204 and isflushed from other subsets, if necessary. The ring network ensurescoherency for shared data. The ring network is bi-directional to allowagents such as processor cores, L2 caches and other logic blocks tocommunicate with each other within the chip. Each ring data-path is1012-bits wide per direction.

FIG. 12B is an expanded view of part of the processor core in FIG. 12Aaccording to embodiments of the invention. FIG. 12B includes an L1 datacache 1206A part of the L1 cache 1204, as well as more detail regardingthe vector unit 1210 and the vector registers 1214. Specifically, thevector unit 1210 is a 16-wide vector processing unit (VPU) (see the16-wide ALU 1228), which executes one or more of integer,single-precision float, and double-precision float instructions. The VPUsupports swizzling the register inputs with swizzle unit 1220, numericconversion with numeric convert units 1222A-B, and replication withreplication unit 1224 on the memory input. Write mask registers 1226allow predicating resulting vector writes.

FIG. 13 is a block diagram of a processor 1300 that may have more thanone core, may have an integrated memory controller, and may haveintegrated graphics according to embodiments of the invention. The solidlined boxes in FIG. 13 illustrate a processor 1300 with a single core1302A, a system agent 1310, a set of one or more bus controller units1316, while the optional addition of the dashed lined boxes illustratesan alternative processor 1300 with multiple cores 1302A-N, a set of oneor more integrated memory controller unit(s) 1314 in the system agentunit 1310, and special purpose logic 1308.

Thus, different implementations of the processor 1300 may include: 1) aCPU with the special purpose logic 1308 being integrated graphics and/orscientific (throughput) logic (which may include one or more cores), andthe cores 1302A-N being one or more general purpose cores (e.g., generalpurpose in-order cores, general purpose out-of-order cores, acombination of the two); 2) a coprocessor with the cores 1302A-N being alarge number of special purpose cores intended primarily for graphicsand/or scientific (throughput); and 3) a coprocessor with the cores1302A-N being a large number of general purpose in-order cores. Thus,the processor 1300 may be a general-purpose processor, coprocessor orspecial-purpose processor, such as, for example, a network orcommunication processor, compression engine, graphics processor, GPGPU(general purpose graphics processing unit), a high-throughput manyintegrated core (MIC) coprocessor (including 30 or more cores), embeddedprocessor, or the like. The processor may be implemented on one or morechips. The processor 1300 may be a part of and/or may be implemented onone or more substrates using any of many process technologies, such as,for example, BiCMOS, CMOS, or NMOS.

The memory hierarchy includes one or more levels of cache within thecores, a set or one or more shared cache units 1306, and external memory(not shown) coupled to the set of integrated memory controller units1314. The set of shared cache units 1306 may include one or moremid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), orother levels of cache, a last level cache (LLC), and/or combinationsthereof. While in one embodiment a ring based interconnect unit 1312interconnects the integrated graphics logic 1308 (integrated graphicslogic 1308 is an example of and is also referred to herein as specialpurpose logic), the set of shared cache units 1306, and the system agentunit 1310/integrated memory controller unit(s) 1314, alternativeembodiments may use any number of well-known techniques forinterconnecting such units. In one embodiment, coherency is maintainedbetween one or more cache units 1306 and cores 1302-A-N.

In some embodiments, one or more of the cores 1302A-N are capable ofmulti-threading. The system agent 1310 includes those componentscoordinating and operating cores 1302A-N. The system agent unit 1310 mayinclude for example a power control unit (PCU) and a display unit. ThePCU may be or include logic and components needed for regulating thepower state of the cores 1302A-N and the integrated graphics logic 1308.The display unit is for driving one or more externally connecteddisplays.

The cores 1302A-N may be homogenous or heterogeneous in terms ofarchitecture instruction set; that is, two or more of the cores 1302A-Nmay be capable of execution the same instruction set, while others maybe capable of executing only a subset of that instruction set or adifferent instruction set.

Exemplary Computer Architectures

FIGS. 14-17 are block diagrams of exemplary computer architectures.Other system designs and configurations known in the arts for laptops,desktops, handheld PCs, personal digital assistants, engineeringworkstations, servers, network devices, network hubs, switches, embeddedprocessors, digital signal processors (DSPs), graphics devices, videogame devices, set-top boxes, micro controllers, cell phones, portablemedia players, hand held devices, and various other electronic devices,are also suitable. In general, a huge variety of systems or electronicdevices capable of incorporating a processor and/or other executionlogic as disclosed herein are generally suitable.

Referring now to FIG. 14, shown is a block diagram of a system 1400 inaccordance with one embodiment of the present invention. The system 1400may include one or more processors 1410, 1415, which are coupled to acontroller hub 1420. In one embodiment the controller hub 1420 includesa graphics memory controller hub (GMCH) 1490 and an Input/output Hub(IOH) 1450 (which may be on separate chips); the GMCH 1490 includesmemory and graphics controllers to which are coupled memory 1440 and acoprocessor 1445; the IOH 1450 couples input/output (I/O) devices 1460to the GMCH 1490. Alternatively, one or both of the memory and graphicscontrollers are integrated within the processor (as described herein),the memory 1440 and the coprocessor 1445 are coupled directly to theprocessor 1410, and the controller hub 1420 in a single chip with theIOH 1450.

The optional nature of additional processors 1415 is denoted in FIG. 14with broken lines. Each processor 1410, 1415 may include one or more ofthe processing cores described herein and may be some version of theprocessor 1300.

The memory 1440 may be, for example, dynamic random access memory(DRAM), phase change memory (PCM), or a combination of the two. For atleast one embodiment, the controller hub 1420 communicates with theprocessor(s) 1410, 1415 via a multi-drop bus, such as a frontside bus(FSB), point-to-point interface such as QuickPath Interconnect (QPI), orsimilar connection 1495.

In one embodiment, the coprocessor 1445 is a special-purpose processor,such as, for example, a high-throughput MIC processor, a network orcommunication processor, compression engine, graphics processor, GPGPU,embedded processor, or the like. In one embodiment, controller hub 1420may include an integrated graphics accelerator.

There can be a variety of differences between the physical resources1410, 1415 in terms of a spectrum of metrics of merit includingarchitectural, microarchitectural, thermal, power consumptioncharacteristics, and the like.

In one embodiment, the processor 1410 executes instructions that controldata processing operations of a general type. Embedded within theinstructions may be coprocessor instructions. The processor 1410recognizes these coprocessor instructions as being of a type that shouldbe executed by the attached coprocessor 1445. Accordingly, the processor1410 issues these coprocessor instructions (or control signalsrepresenting coprocessor instructions) on a coprocessor bus or otherinterconnect, to coprocessor 1445. Coprocessor(s) 1445 accept andexecute the received coprocessor instructions.

Referring now to FIG. 15, shown is a block diagram of a first morespecific exemplary system 1500 in accordance with an embodiment of thepresent invention. As shown in FIG. 15, multiprocessor system 1500 is apoint-to-point interconnect system, and includes a first processor 1570and a second processor 1580 coupled via a point-to-point interconnect1550. Each of processors 1570 and 1580 may be some version of theprocessor 1300. In one embodiment of the invention, processors 1570 and1580 are respectively processors 1410 and 1415, while coprocessor 1538is coprocessor 1445. In another embodiment, processors 1570 and 1580 arerespectively processor 1410 coprocessor 1445.

Processors 1570 and 1580 are shown including integrated memorycontroller (IMC) units 1572 and 1582, respectively. Processor 1570 alsoincludes as part of its bus controller units point-to-point (P-P)interfaces 1576 and 1578; similarly, second processor 1580 includes P-Pinterfaces 1586 and 1588. Processors 1570, 1580 may exchange informationvia a point-to-point (P-P) interface 1550 using P-P interface circuits1578, 1588. As shown in FIG. 15, IMCs 1572 and 1582 couple theprocessors to respective memories, namely a memory 1532 and a memory1534, which may be portions of main memory locally attached to therespective processors.

Processors 1570, 1580 may each exchange information with a chipset 1590via individual P-P interfaces 1552, 1554 using point to point interfacecircuits 1576, 1594, 1586, 1598. Chipset 1590 may optionally exchangeinformation with the coprocessor 1538 via a high-performance interface1592. In one embodiment, the coprocessor 1538 is a special-purposeprocessor, such as, for example, a high-throughput MIC processor, anetwork or communication processor, compression engine, graphicsprocessor, GPGPU, embedded processor, or the like.

A shared cache (not shown) may be included in either processor oroutside of both processors, yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 1590 may be coupled to a first bus 1516 via an interface 1596.In one embodiment, first bus 1516 may be a Peripheral ComponentInterconnect (PCI) bus, or a bus such as a PCI Express bus or anotherthird generation I/O interconnect bus, although the scope of the presentinvention is not so limited.

As shown in FIG. 15, various I/O devices 1514 may be coupled to firstbus 1516, along with a bus bridge 1518 which couples first bus 1516 to asecond bus 1520. In one embodiment, one or more additional processor(s)1515, such as coprocessors, high-throughput MIC processors, GPGPU's,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessor, are coupled to first bus 1516. In one embodiment, second bus1520 may be a low pin count (LPC) bus. Various devices may be coupled toa second bus 1520 including, for example, a keyboard and/or mouse 1522,communication devices 1527 and a storage unit 1528 such as a disk driveor other mass storage device which may include instructions/code anddata 1530, in one embodiment. Further, an audio I/O 1524 may be coupledto the second bus 1520. Note that other architectures are possible. Forexample, instead of the point-to-point architecture of FIG. 15, a systemmay implement a multi-drop bus or other such architecture.

Referring now to FIG. 16, shown is a block diagram of a second morespecific exemplary system 1600 in accordance with an embodiment of thepresent invention. Like elements in FIGS. 15 and 16 bear like referencenumerals, and certain aspects of FIG. 15 have been omitted from FIG. 16in order to avoid obscuring other aspects of FIG. 16.

FIG. 16 illustrates that the processors 1570, 1580 may includeintegrated memory and I/O control logic (“CL”) 1572 and 1582,respectively. Thus, the CL 1572, 1582 include integrated memorycontroller units and include I/O control logic. FIG. 16 illustrates thatnot only are the memories 1532, 1534 coupled to the CL 1572, 1582, butalso that I/O devices 1614 are also coupled to the control logic 1572,1582. Legacy I/O devices 1615 are coupled to the chipset 1590.

Referring now to FIG. 17, shown is a block diagram of a SoC 1700 inaccordance with an embodiment of the present invention. Similar elementsin FIG. 13 bear like reference numerals. Also, dashed lined boxes areoptional features on more advanced SoCs. In FIG. 17, an interconnectunit(s) 1702 is coupled to: an application processor 1710 which includesa set of one or more cores 1302A-N, which include cache units 1304A-N,and shared cache unit(s) 1306; a system agent unit 1310; a buscontroller unit(s) 1316; an integrated memory controller unit(s) 1314; aset or one or more coprocessors 1720 which may include integratedgraphics logic, an image processor, an audio processor, and a videoprocessor; an static random access memory (SRAM) unit 1730; a directmemory access (DMA) unit 1732; and a display unit 1740 for coupling toone or more external displays. In one embodiment, the coprocessor(s)1720 include a special-purpose processor, such as, for example, anetwork or communication processor, compression engine, GPGPU, ahigh-throughput MIC processor, embedded processor, or the like.

Embodiments of the mechanisms disclosed herein may be implemented inhardware, software, firmware, or a combination of such implementationapproaches. Embodiments of the invention may be implemented as computerprograms or program code executing on programmable systems comprising atleast one processor, a storage system (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device.

Program code, such as code 1530 illustrated in FIG. 15, may be appliedto input instructions to perform the functions described herein andgenerate output information. The output information may be applied toone or more output devices, in known fashion. For purposes of thisapplication, a processing system includes any system that has aprocessor, such as, for example; a digital signal processor (DSP), amicrocontroller, an application specific integrated circuit (ASIC), or amicroprocessor.

The program code may be implemented in a high level procedural or objectoriented programming language to communicate with a processing system.The program code may also be implemented in assembly or machinelanguage, if desired. In fact, the mechanisms described herein are notlimited in scope to any particular programming language. In any case,the language may be a compiled or interpreted language.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores,” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Such machine-readable storage media may include, without limitation,non-transitory, tangible arrangements of articles manufactured or formedby a machine or device, including storage media such as hard disks, anyother type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritables (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMS) such as dynamic random accessmemories (DRAMs), static random access memories (SRAMs), erasableprogrammable read-only memories (EPROMs), flash memories, electricallyerasable programmable read-only memories (EEPROMs), phase change memory(PCM), magnetic or optical cards, or any other type of media suitablefor storing electronic instructions.

Accordingly, embodiments of the invention also include non-transitory,tangible machine-readable media containing instructions or containingdesign data, such as Hardware Description Language (HDL), which definesstructures, circuits, apparatuses, processors and/or system featuresdescribed herein. Such embodiments may also be referred to as programproducts.

Emulation (Including Binary Translation, Code Morphing, Etc.)

In some cases, an instruction converter may be used to convert aninstruction from a source instruction set to a target instruction set.For example, the instruction converter may translate (e.g., using staticbinary translation, dynamic binary translation including dynamiccompilation), morph, emulate, or otherwise convert an instruction to oneor more other instructions to be processed by the core. The instructionconverter may be implemented in software, hardware, firmware, or acombination thereof. The instruction converter may be on processor, offprocessor, or part on and part off processor.

FIG. 18 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to embodimentsof the invention. In the illustrated embodiment, the instructionconverter is a software instruction converter, although alternativelythe instruction converter may be implemented in software, firmware,hardware, or various combinations thereof. FIG. 18 shows a program in ahigh level language 1802 may be compiled using an x86 compiler 1804 togenerate x86 binary code 1806 that may be natively executed by aprocessor with at least one x86 instruction set core 1816. The processorwith at least one x86 instruction set core 1816 represents any processorthat can perform substantially the same functions as an Intel processorwith at least one x86 instruction set core by compatibly executing orotherwise processing (1) a substantial portion of the instruction set ofthe Intel x86 instruction set core or (2) object code versions ofapplications or other software targeted to run on an Intel processorwith at least one x86 instruction set core, in order to achievesubstantially the same result as an Intel processor with at least onex86 instruction set core. The x86 compiler 1804 represents a compilerthat is operable to generate x86 binary code 1806 (e.g., object code)that can, with or without additional linkage processing, be executed onthe processor with at least one x86 instruction set core 1816.Similarly, FIG. 18 shows the program in the high level language 1802 maybe compiled using an alternative instruction set compiler 1808 togenerate alternative instruction set binary code 1810 that may benatively executed by a processor without at least one x86 instructionset core 1814 (e.g., a processor with cores that execute the MIPSinstruction set of MIPS Technologies of Sunnyvale, Calif. and/or thatexecute the ARM instruction set of ARM Holdings of Sunnyvale, Calif.).The instruction converter 1812 is used to convert the x86 binary code1806 into code that may be natively executed by the processor without anx86 instruction set core 1814. This converted code is not likely to bethe same as the alternative instruction set binary code 1810 because aninstruction converter capable of this is difficult to make; however, theconverted code will accomplish the general operation and be made up ofinstructions from the alternative instruction set. Thus, the instructionconverter 1812 represents software, firmware, hardware, or a combinationthereof that, through emulation, simulation or any other process, allowsa processor or other electronic device that does not have an x86instruction set processor or core to execute the x86 binary code 1806.

Further Examples

Example 1 provides an exemplary processor to execute an asymmetric fusedmultiply-add (FMA) instruction, including: fetch circuitry to fetch anFMA instruction having fields to specify an opcode, a destination, andfirst and second source vectors having first and second widths,respectively; decode circuitry to decode the fetched FMA instruction;and a single instruction multiple data (SIMD) execution circuit toexecute the decoded FMA instruction by processing as many elements ofthe second source vector as fit into an SIMD lane width by multiplyingeach element by a corresponding element of the first source vector, andaccumulating a resulting product with previous contents of thedestination; wherein the SIMD lane width is one of 16 bits, 32 bits, and64 bits, the first width is one of 4 bits and 8 bits, and the secondwidth is one of 1 bit, 2 bits, and 4 bits.

Example 2 includes the substance of the exemplary processor of Example1, wherein the SIMD execution circuit processes the as many elementsconcurrently.

Example 3 includes the substance of the exemplary processor of Example1, wherein the SIMD execution circuit processes the as many elements ina single clock cycle.

Example 4 includes the substance of the exemplary processor of Example1, wherein the SIMD execution circuit uses a plurality of fusedmultiply-add (FMA) hardware units to process the maximal number ofelements, the plurality of FMA hardware units either being arranged inparallel or cascaded.

Example 5 includes the substance of the exemplary processor of Example1, wherein the first and second widths are specified by the opcode.

Example 6 includes the substance of the exemplary processor of Example1, wherein the FMA instruction further specifies a repeat indicatorbeing one of two, four, and eight, the specified destination includes avector, and the SIMD execution circuit uses a plurality of SIMD lanes toconcurrently repeat the execution a number of times as specified by therepeat indicator, each time writing the accumulated result to adifferent element of the destination vector.

Example 7 includes the substance of the exemplary processor of Example1, wherein the SIMD execution circuit further rounds the accumulation ofthe resulting product and the previous contents of the destination tofit within a number of bits of the destination.

Example 8 includes the substance of the exemplary processor of Example7, wherein the processor further includes a software-accessible controlregister to store a rounding control, wherein the SIMD execution circuitperforms the rounding according to the rounding control, wherein therounding control specifies one of round to nearest with ties to even,round to nearest with ties away from zero, round toward 0, round towardpositive infinity, and round toward negative infinity.

Example 9 includes the substance of the exemplary processor of Example1, wherein the SIMD execution circuit further checks for saturation andsaturates the accumulation of the resulting product and the previouscontents of the destination to a predefined maximum value.

Example 10 includes the substance of the exemplary processor of Example9, further including a software-accessible status register to be used bythe SIMD execution circuit to report occurrence of saturation tosoftware.

Example 11 provides an exemplary method of executing an asymmetric fusedmultiply-add (FMA) instruction, including: fetching, by fetch circuitry,an FMA instruction having fields to specify an opcode, a destination,and first and second source vectors having first and second widths,respectively; decoding, by decode circuitry, the fetched FMAinstruction; and executing, by a single instruction multiple data (SIMD)execution circuit, the decoded FMA instruction by processing as manyelements of the second source vector as fit into an SIMD lane width bymultiplying each element by a corresponding element of the first sourcevector, and accumulating a resulting product with previous contents ofthe destination; wherein the SIMD lane width is one of 16 bits, 32 bits,and 64 bits, the first width is one of 4 bits and 8 bits, and the secondwidth is one of 1 bit, 2 bits, and 4 bits.

Example 12 includes the substance of the exemplary method of Example 11,wherein the SIMD execution circuit processes the as many elementsconcurrently.

Example 13 includes the substance of the exemplary method of Example 11,wherein the SIMD execution circuit processes the as many elements in asingle clock cycle.

Example 14 includes the substance of the exemplary method of Example 11,wherein the SIMD execution circuit uses a plurality of fusedmultiply-add (FMA) hardware units to process the maximal number ofelements, the plurality of FMA hardware units either being arranged inparallel or cascaded.

Example 15 includes the substance of the exemplary method of Example 11,wherein the first and second widths are specified by the opcode.

Example 16 includes the substance of the exemplary method of Example 11,wherein the FMA instruction further specifies a repeat indicator beingone of two, four, and eight, the specified destination includes avector, further including the SIMD execution circuit using a pluralityof SIMD lanes to concurrently repeat the execution a number of times asspecified by the repeat indicator, each time writing the accumulatedresult to a different element of the destination vector.

Example 17 includes the substance of the exemplary method of Example 11,further including rounding, by the SIMD execution circuit, theaccumulation of the resulting product and the previous contents of thedestination to fit within a number of bits of the destination.

Example 18 includes the substance of the exemplary method of Example 17,wherein the SIMD execution circuit performs the rounding according to arounding control in a software-accessible control register, the roundingcontrol specifying one of round to nearest with ties to even, round tonearest with ties away from zero, round toward 10, round toward positiveinfinity, and round toward negative infinity.

Example 19 includes the substance of the exemplary method of Example 11,further including checking for saturation, by the SIMD executioncircuit, and saturating the accumulation of the resulting product andthe previous contents of the destination to a predefined maximum value.

Example 20 includes the substance of the exemplary method of Example 19,further including using, by the SIMD execution circuit, asoftware-accessible status register to report occurrence of saturationto software.

What is claimed is:
 1. A processor comprising: fetch circuitry to fetcha fused multiply-accumulate (FMA) instruction having a plurality offields usable to identify an opcode, a first input value, a second inputvalue, and a third input value, wherein the first and the second inputvalues each comprise first and second sets of vector data elements,respectively, wherein each of the vector data elements of at least thesecond set of vector data elements has an M-bit width, wherein the thirdinput value comprises an N-bit accumulation value, where N is an integermultiple of M; decode circuitry to decode the FMA instruction; and asingle instruction multiple data (SIMD) execution circuit to execute theFMA instruction in an N-bit SIMD lane, the SIMD execution circuit tosimultaneously multiply each data element of the second set of vectordata elements by a corresponding data element of the first set of vectordata elements to produce a plurality of temporary products, and to addthe temporary products to the N-bit accumulation value to produce anN-bit result value; wherein the N-bit SIMD lane is one of a 16-bit lane,a 32-bit lane, and a 64-bit lane, and the M-bit width comprises one of a4-bit width and an 8-bit width.
 2. The processor of claim 1, whereineach of the vector data elements of at least the first set of vectordata elements has at least one of a 1-bit width, a 2-bit width, and a4-bit width.
 3. The processor of claim 1, wherein the second set ofvector data elements comprises N/M data elements, and wherein the SIMDexecution circuit is to simultaneously perform N/M multiplications. 4.The processor of claim 3, wherein N=32 and M=4.
 5. The processor ofclaim 1, wherein the SIMD execution circuit comprises a first SIMDexecution circuit and the N-bit SIMD lane comprises a first N-bit SIMDlane, the processor further comprising: a second SIMD execution circuitto execute the FMA instruction in a second N-bit SIMD lane.
 6. Theprocessor of claim 5, further comprising: a plurality of additional SIMDexecution circuits to execute the FMA instruction in a correspondingplurality of additional SIMD lanes.
 7. The processor of claim 1, whereineach of the vector data elements of the first set of vector dataelements and the second set of vector data elements comprisefloating-point data elements.
 8. A method comprising: fetching a fusedmultiply-accumulate (FMA) instruction having a plurality of fieldsusable to identify an opcode, a first input value, a second input value,and a third input value, wherein the first and the second input valueseach comprise first and second sets of vector data elements,respectively, wherein each of the vector data elements of at least thesecond set of vector data elements has an M-bit width, wherein the thirdinput value comprises an N-bit accumulation value, where N is an integermultiple of M; decoding the FMA instruction; and executing, by a singleinstruction multiple data (SIMD) execution circuit, the FMA instructionin an N-bit SIMD lane, wherein executing comprises multiplying each dataelement of the second set of vector data elements by a correspondingdata element of the first set of vector data elements to produce aplurality of temporary products, and adding the temporary products tothe N-bit accumulation value to produce an N-bit result value; whereinthe N-bit SIMD lane is one of a 16-bit lane, a 32-bit lane, and a 64-bitlane, and the M-bit width comprises one of a 4-bit width and an 8-bitwidth.
 9. The method of claim 8, wherein each of the vector dataelements of at least the first set of vector data elements has at leastone of a 1-bit width, a 2-bit width, and a 4-bit width.
 10. The methodof claim 8, wherein the second set of vector data elements comprises N/Mdata elements, and wherein the SIMD execution circuit is tosimultaneously perform N/M multiplications.
 11. The method of claim 10,wherein N=32 and M=4.
 12. The method of claim 8, wherein the SIMDexecution circuit comprises a first SIMD execution circuit and the N-bitSIMD lane comprises a first N-bit SIMD lane, the method furthercomprising: executing, by a second SIMD execution circuit, the FMAinstruction in a second N-bit SIMD lane.
 13. The method of claim 12,further comprising: executing, by a plurality of additional SIMDexecution circuits, the FMA instruction in a corresponding plurality ofadditional SIMD lanes.
 14. The method of claim 8, wherein each of thevector data elements of the first set of vector data elements and thesecond set of vector data elements comprise floating-point dataelements.
 15. A machine-readable medium having program code storedthereon which, when executed by a machine, causes the machine to performoperations of: fetching a fused multiply-accumulate (FMA) instructionhaving a plurality of fields usable to identify an opcode, a first inputvalue, a second input value, and a third input value, wherein the firstand the second input values each comprise first and second sets ofvector data elements, respectively, wherein each of the vector dataelements of at least the second set of vector data elements has an M-bitwidth, wherein the third input value comprises an N-bit accumulationvalue, where N is an integer multiple of M; decoding the FMAinstruction; and executing, by a single instruction multiple data (SIMD)execution circuit, the FMA instruction in an N-bit SIMD lane, whereinexecuting comprises multiplying each data element of the second set ofvector data elements by a corresponding data element of the first set ofvector data elements to produce a plurality of temporary products, andadding the temporary products to the N-bit accumulation value to producean N-bit result value; wherein the N-bit SIMD lane is one of a 16-bitlane, a 32-bit lane, and a 64-bit lane, and the M-bit width comprisesone of a 4-bit width and an 8-bit width.
 16. The machine-readable mediumof claim 15, wherein each of the vector data elements of at least thefirst set of vector data elements has at least one of a 1-bit width, a2-bit width, and a 4-bit width.
 17. The machine-readable medium of claim15, wherein the second set of vector data elements comprises N/M dataelements, and wherein the SIMD execution circuit is to simultaneouslyperform N/M multiplications.
 18. The machine-readable medium of claim15, wherein N=32 and M=4.
 19. The machine-readable medium of claim 15,wherein the SIMD execution circuit comprises a first SIMD executioncircuit and the N-bit SIMD lane comprises a first N-bit SIMD lane, themachine-readable medium further comprising program code to cause themachine to perform the operations of: executing, by a second SIMDexecution circuit, the FMA instruction in a second N-bit SIMD lane. 20.The machine-readable medium of claim 19, further comprising program codeto cause the machine to perform the operations of: executing, by aplurality of additional SIMD execution circuits, the FMA instruction ina corresponding plurality of additional SIMD lanes.
 21. Themachine-readable medium of claim 15, wherein each of the vector dataelements of the first set of vector data elements and the second set ofvector data elements comprise floating-point data elements.